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Identification of novel prognostic biomarkers for thyroid cancer by integrated transcriptome analysis of metastasis-associated genes

GSE284225 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/12/19 Platform GPL23479
At a glance

RNA-seq (gene expression) · mouse · 12 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
12 samples: 2 × BVE-Met vs 2 × BVEp53-Met vs 2 × Kras-Met vs 2 × Krasp16-MetGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Identification of novel prognostic biomarkers for thyroid cancer by integrated transcriptome analysis of metastasis-associated genesFrontiers in oncology · 2025
Summary (written by the submitters)
Distant metastasis (DM) is the most important prognostic factor affecting overall survival (OS) of thyroid cancer. The current study aimed to discover prognostic biomarkers to predict thyroid cancer survival, particularly PTC, the most common subtype of thyroid cancer. Four RNA-seq datasets of experimental lung metastasis from 4 transgenic mouse models of PTC, follicular thyroid cancer (FTC), poorly-differentiated thyroid cancer (PDTC), and anaplastic thyroid cancer (ATC) were integrated to screen for candidate genes involved in DM.
Read the full summary ↓
TCGA-THCA dataset were used to validate the candidate genes. A total of 105 up-regulated and 25 down-regulated differentially expressed genes (DEGs) were identified to be present in all 4 datasets. Regulation of cytokine production, inflammation, immune checkpoint regulation, MAPK/ERK cascade were major enriched pathways in metastatic tumor cells. We identified 7 genes whose overexpression was present in 63 of 498 PTC samples (13%) and was associated with poor OS (p<0.01). Clinically, the 7–gene expression signature was associated with older age at the diagnosis, late stage of tumor, tall-cell variant, and higher aneuploidy and hypoxia score. Mutation load was increased in samples with 7–gene expression signature: 26 samples had more than one driver mutations (47%, 26/55). Deep deletions in other chromosomal locus were frequently found in samples with BRAFV600E point mutations. In contrast, only 7% samples without the 7-gene expression signature had more than one driver mutations (24/243). Increased copy number variants (CNVs) were also observed in metastatic as compared to primary tumor cells such as large deletions and duplications. We conclude that the 7–gene expression signature is associated with poor prognosis and chromosomal instability. It may be a useful biomarker for risk stratification for DM and help decision-making in initial surgical recommendations.
Published in
Identification of novel prognostic biomarkers for thyroid cancer by integrated transcriptome analysis of metastasis-associated genes
Alnwisser B, Alshehri S, Qattan A et al. · Frontiers in oncology 2025 · PMID 40458726 · doi:10.3389/fonc.2025.1536270
This dataset
Open GSE284225 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE284225_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1198026 and SRA study SRP551317. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40458726) ↗
Study design
2 × BVE-Met vs 2 × BVEp53-Met vs 2 × Kras-Met vs 2 × Krasp16-Met

Supports a between-group comparison across 8 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 8 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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